Abstract

At present, all CNN-based fire detection algorithms identify fire by means of a single frame image, all of which demonstrate low accuracy under strong interferences or complex backgrounds such as flickering light or backgrounds with high level of brightness. To increase the accuracy of fire detection, this paper presents a neural network model which combines lightweight CNN with SRU. In this algorithm, the scene content is extracted by CNN and the dynamic characteristics of the flames are extracted from sequential frames. In this paper, Resnet18+SRU (V1-type) and Mobilenets+SRU (V2-type) are proposed. Based on the characteristics of flames at a fixed position within a short period of time, a 3D convolutional layer is added between the Mobilenets and the SRU in the V2-type model, resulting in the V3-type model. Based on a cross validation set containing multiple types of interference in an indoor environment, experiments were conducted to compare the three models proposed in this paper with other models. The experiment results showed that the accuracy of the method proposed in this paper is above 96%, about 25% higher than the accuracy of CNN-based fire alarm via single-frame image, and that the V3-type models with 3D convolutional layer has the highest accuracy and best overall performance.

中文翻译：

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基于轻型卷积神经网络的室内视频火焰检测

摘要

目前，所有基于CNN的火灾探测算法均通过单帧图像识别火灾，所有这些算法在强烈干扰或复杂背景（例如闪烁的光线或高亮度背景）下均显示出较低的准确性。为了提高火灾探测的准确性，本文提出了一种将轻量级CNN与SRU相结合的神经网络模型。该算法通过CNN提取场景内容，并从连续帧中提取火焰的动态特性。本文提出了Resnet18 + SRU（V1型）和Mobilenets + SRU（V2型）。根据短时间内固定位置的火焰特性，在V2型模型的Mobilenets和SRU之间添加3D卷积层，从而形成V3型模型。基于在室内环境中包含多种干扰的交叉验证集，进行了实验，以将本文提出的三种模型与其他模型进行比较。实验结果表明，本文提出的方法的准确性高于96％，比基于单帧图像的基于CNN的火灾报警的准确性高约25％，并且具有3D卷积层的V3型模型具有最高的精度和最佳的整体性能。